Regional disparities in Dementia-free Life Expectancy in Japan: An ecological study, using the Japanese long-term care insurance claims database

Background The number of people with dementia increases in an aging society; therefore, promoting policies for dementia throughout the community is crucial to creating a dementia-friendly society. Understanding the status of older adults with dementia in each region of Japan will be a helpful indicator. We calculated Dementia-free Life Expectancy and aimed to examine regional disparities and their associated factors. Methods We calculated Dementia-free Life Expectancy and Life Expectancy with Dementia for each secondary medical area in Japan based on the Degree of Independence in Daily Living for the Demented Elderly, using data extracted from the Japanese long-term care insurance claims database. We then conducted a partial least squares regression analysis, the objective variables being Dementia-free Life Expectancy and Life Expectancy with Dementia for both sexes at age 65, and explanatory regional-level variables included demographic, socioeconomic, and healthcare resources variables. Results The mean estimated regional-level Dementia-free Life Expectancy at age 65 was 17.33 years (95% confidence interval [CI] 17.27–17.38) for males and 20.05 years (95% CI 19.99–20.11) for females. Three latent components identified by partial least squares regression analysis represented urbanicity, socioeconomic conditions, and health services-related factors of the secondary medical areas. The second component explained the most variation in Dementia-free Life Expectancy of the three, indicating that higher socioeconomic status was associated with longer Dementia-free Life Expectancy. Conclusions There were regional disparities in secondary medical area level Dementia-free Life Expectancy. Our results suggest that socioeconomic conditions are more related to Dementia-free Life Expectancy than urbanicity and health services-related factors.


Introduction
. Factors related to regional characteristics, denominators used in calculations, and data sources.

Geographical units
Secondary medical areas (SMAs), which consist of several municipalities, were used as the unit of this analysis. Japan's Medical Care Act stipulates SMAs are the units that take comprehensive measures to foster cooperation between health care, medical care, and welfare. There are 335 SMAs in Japan, with a mean population of about 4 million in each SMA. We aggregated public statistical data by SMA and utilized it in the analysis. We used data in 2017, but data submission from long-term care insurers became mandatory after 2018. In 2017, some insurers did not submit data or had significantly fewer data. To omit those insurers, we checked the list of long-term care insurers against the specific extraction data and identified 334 SMAs. We excluded one SMA with missing data from the 2015 census owing to the Great East Japan Earthquake and included 333 SMAs in the analysis.

Dementia-free Life Expectancy
In our study, De-FLE refers to the period before dementia symptoms become severe enough to require support from others. We also defined Life Expectancy (LE) with Dementia as the period in which people receive long-term care due to cognitive difficulties, representing the period between life expectancy and De-FLE. To calculate these metrics, we utilized the "Degree of Independence in Daily Living for the Demented Elderly," which was created by the MHLW to facilitate objective and quick assessments by public health nurses, nurses, social workers, and care workers [13]. When the elderly in need apply for LTCI certification, investigators designated by the municipality visit their place of residence and judge their level of independence. Their family physicians also use the criteria to evaluate their independence level. The Degree of Independence in Daily Living for the Demented Elderly classifies applicants into five categories regarding their communication capacity, symptoms, and behaviors (S1 Table). In this study, we defined "unhealthy" as Independence Level II or higher to calculate De-FLE. Those assessed as Independence Level II usually live at home, but since it is challenging for them to live alone as they need support from the people around them, they often use home services in the daytime. We extracted data from the Japanese long-term care insurance claims database in 2017 provided by the MHLW and calculated De-FLE using the number of independent degrees of daily living for the demented elderly. We defined LE with Dementia as the period between life expectancy and healthy life expectancy. The calculation followed the "Healthy Life Expectancy Calculation Program" published by the MHLW research group [14].

Statistical analysis
We conducted a partial least squares (PLS) regression with SMA-level De-FLE and LE with Dementia for both sexes at age 65 in 2017 as the objective variables and regional factors as explanatory variables. We selected explanatory variables based on previous studies and the World Health Organization Guidelines on Dementia: "Risk Factors for Dementia," and then aggregated them by SMAs, as shown in Table 1 [15].
PLS regression analysis is commonly used in econometrics, and in recent years, a few public health studies have adopted it [16,17]. This model first extracts the latent components to have the largest covariance with the objective variables, then uses them to perform regression analysis. It can deal with multicollinearity among variables, making it useful for cases with many correlated explanatory variables [18]. We identified the number of latent factors as follows. First, we used leave-one-out cross-validation to identify the number of factors that minimizes the predicted residual sum of squares (PRESS) by this process [19]. The model with the number of latent factors identified here does not often show significant differences from a model with fewer factors. We performed Van der Voet's test to test whether the difference was significant. Van der Voet's test is a statistical test that compares the predicted residuals of different models [20]. We finally identified the least number of latent factors among the models that none were significantly different from the model with the smallest PRESS. All statistical analyses were conducted using JMP Pro 16.1 (SAS Institute Inc., Cary, NC).
This study was conducted in accordance with Ethical Guidelines for Medical and Biological Research Involving Human Subjects and the study was approved by the Ethics Committee, Kyoto University Graduate School and Faculty of Medicine (approval number: R0438) with a waiver of informed consent prior to data collection.

PLS regression analysis
Cross-validation results showed that the PRESS was minimal when 15 factors were extracted. Then, three components were finally identified based on the outcome of Van der Vote's Test. Factor loadings and the variable importance in projection (VIP) for each of the three factors are shown in Table 3. The factor loadings in Table 3 show that Component 1 was positively loaded by the proportion of junior college or university graduates, the proportion of commuters traveling by railway or train, and the financial power index. In contrast, Component 1 was negatively loaded by the proportion of persons employed in primary industry and the proportion of elderly in the population. Thus, we named this component urbanicity. Component 2 was positively loaded by the proportion of persons employed in secondary industry and mean household size, while it was negatively loaded by the proportion of single households, tobacco taxes revenue per population, unemployment rate, and the proportion of single-mother households. We defined this as the socioeconomic conditions-related component. Component 3 was characterized by positive loadings on the number of general clinics per 100,000 people and the number of hospital beds per 1,000 people, and thereby named the healthcare resources component. The high score of each component represented the following variables: Component 1, high urbanization; Component 2, good socioeconomic conditions; and Component 3, rich healthcare resources.
Of the three components in Table 3, Component 1 had the largest coefficient of determination for the explanatory variable (31.2%), and Component 2 had the largest coefficient of determination for the objective variable (6.67%), which meant that Component 2 explained De-FLE the best of the three components. Fig 1(A) shows the correlation and loading plot of variables for Components 1 and 2. De-FLE for males and LE with Dementia for both sexes were positively correlated with the score of Component 1. De-FLE for both sexes was positively correlated with the score of Component 2, especially for males. Some variables with high negative loadings with Component 2 had nearly zero loading on Component 1 (e.g., tobacco revenue per population and unemployment rate) . Fig 1(B) and 1(C) show the correlation and loading plots of Components 2/3 and 1/3,

Discussion
In this study, we calculated SMA-level De-FLE and LE with Dementia for both sexes and then conducted PLS regression analysis to examine regional disparities in Japan. De-FLE and LE with Dementia varied among SMAs, and the mean SMA-level De-FLE was longer for women than men. Three latent components were identified, which represented urbanicity, socioeconomic conditions, and healthcare resources. Socioeconomic conditions explained De-FLE the best. Component 1 explained the region's characteristics well, but its contribution to De-FLE can be described as small based on the coefficient of determination. The score of Component 2 had a positive correlation with De-FLE. Therefore, De-FLE will be longer for persons living in better socioeconomic conditions. Component 2 can be referred to as the most crucial component in explaining De-FLE because it had the largest coefficient of determination for the objective variables of all three components. This result was consistent with the well-known fact that maintaining good socioeconomic status is essential to staying healthy [21]. In addition, an Improvement in living conditions will prolong De-FLE. The score of Component 3 was positively correlated with De-FLE, suggesting that better access to healthcare resources was also positively associated with longer De-FLE. The development of a healthcare delivery system may result in longer De-FLE [22,23].
The score of Component 1 was positively correlated with LE with Dementia for both sexes. Several studies in the US have reported higher mortality among people living in non-metropolitan than in metropolitan areas [24,25]. Component 2 had a negative and weak correlation with LE with Dementia, contrary to De-FLE. Socioeconomic conditions may affect De-FLE more than LE with Dementia. The score of Component 3 was positively correlated with LE with Dementia, suggesting that access to healthcare resources was also positively associated with these metrics. A large number of people with dementia might bring about the need to develop a healthcare delivery system, thereby leading to an increase in physicians. However, a causal relationship remains unclear, and further research is warranted.
As discussed above, socioeconomic conditions best explain the variation in De-FLE, compared with urbanicity and healthcare resources. Most of the variables that illustrate Component 2 well had little loadings on Component 1, as shown in Fig 1. For example, variables with high negative loadings on Component 2 were the unemployment rate, the proportion of single households, and tobacco taxes revenue per population. These variables had little loadings on Component 1, meaning that these variables were negatively associated with De-FLE regardless of urbanicity. On the other hand, variables with high positive loadings on Component 2 were the mean household size and the number of dementia supporters per elderly person in the population, implying that they were also positively associated with De-FLE. These also had little loadings on Component 1, which means that they are positively associated with De-FLE in both urban and rural areas. We will further discuss socioeconomic conditions below. The unemployment rate was a critical variable of Component 2. A previous study suggested a significant relationship between work and self-rated health among older adults [26]. A sense of fulfillment is also necessary because working only for financial gain diminishes the health benefits of working after retirement [27]. Measures to reduce the unemployment rate by sufficiently arranging rewarding jobs might work toward extending De-FLE.
Tobacco taxes revenue per population was also a crucial variable of socioeconomic conditions. The adverse effects of tobacco on overall health, not just dementia, have led to the enforcement of many tobacco control and policy measures [28,29]. Social factors such as poor housing conditions, low wages, isolated parents, unemployment, and homelessness are closely linked to high rates of smoking and low rates of smoking cessation, and smoking is a significant cause of illness and premature death [30]. Given the above report and our study, smoking will not only directly but indirectly harm a person's health status and De-FLE.
The number of dementia supporters per elderly person in the population also explains the characteristics of socioeconomic conditions. Previous studies revealed that extensive community resources contribute to longer healthy life expectancy [31,32]. Improved initiatives, such as introducing dementia supporters, may play a critical role in encouraging government and community residents to better understand people with dementia, which will help prevent isolation among the elderly [33].
Variables representing family structure, such as the proportion of older single households and mean household size, were influential factors in explaining socioeconomic conditions. Previous research has found that household size was negatively associated with mortality from dementia [34]. Another previous study noted that people who live alone are more likely to move into a nursing home than those who live with a family member [35]. Our study suggests that household size may also be negatively associated with the number of people needing care due to dementia. However, it is estimated that the proportion of single households will continue to increase, accounting for 40% of all households by 2040 [36]. Given this social trend, promoting the development of community-based support systems will be imperative to avoid burdening family members and ensure that everyone receives appropriate care.
As for gender differences, the correlation between Component 1 and De-FLE was positive for males and nearly zero for females, which means urbanicity could affect males and females differently. Previous studies on the association between city size and health showed that areas with higher urbanicity had more people aged 100 years and over and a lower risk of physical disability [37][38][39]. In addition, Component 2, which represents socioeconomic conditions, correlated with De-FLE better for males than females. Several reports have found gender differences in the association between socioeconomic conditions and healthy life expectancy [40,41]. This study found that socioeconomic conditions explain De-FLE particularly well for males, suggesting the need for community support considering gender.

Strengths and limitations
We examined regional disparities in the time before the elderly needed substantial long-term care. Using LTCI data and official statistical data allowed us to make comparative observations not only for specific regions but also across all regions of Japan. On the other hand, there are some limitations to this study. First, an ecological fallacy, a phenomenon in which observed associations true at the population level do not apply at the individual level, might occur in this study. We considered variables reported to associate with individual-level health status in previous studies and found consistent results. In addition, this study aimed to identify factors associated with regional differences in De-FLE rather than to explore individual-level factors. Furthermore, regional variables, such as the proportion of unmarried people and educational background, may not fully apply to the population over 65. Therefore, we used such regional variables to measure a neighborhood environment rather than directly describing the nature of the older population. Finally, some official statistical data, such as educational background and commuting patterns, were collected from the 2010 Census results, and the situation may have changed since that time. Continuous monitoring with new data is warranted.

Conclusions
This study calculated Dementia-free Life Expectancy at the secondary medical area level as a new indicator for understanding the regional statuses of the elderly with dementia across the entire country of Japan and explored its association with regional variables. We found that socioeconomic conditions correlate with Dementia-free Life Expectancy more than urbanicity and healthcare resources factors. In order to achieve the extension of dementia-free life expectancy and the elimination of its regional disparities, it may be effective for the national and local governments to offer more comprehensive socioeconomic support and make efforts to gain the understanding and cooperation of local communities.
Supporting information S1

Author Contributions
Data curation: Etsu Goto.